###### Tissue

lamindb provides access to the following public Tissue ontologies
through bionty:

1. Uberon

Here we show how to access and search Tissue ontologies to standardize
new data.

 import bionty as bt
 import pandas as pd

##### PublicOntology objects

Let us create a public ontology accessor with ".public" method, which
chooses a default public ontology source from "Source". It's a
PublicOntology object, which you can think about as a public registry:

 tissues = bt.Tissue.public(organism="all")
 tissues

As for registries, you can export the ontology as a "DataFrame":

 df = tissues.to_dataframe()
 df.head()

Unlike registries, you can also export it as a Pronto object via
"public.ontology".

##### Look up terms

As for registries, terms can be looked up with auto-complete:

 lookup = tissues.lookup()

The "." accessor provides normalized terms (lower case, only contains
alphanumeric characters and underscores):

 lookup.alveolus_of_lung

To look up the exact original strings, convert the lookup object to
dict and use the "[]" accessor:

 lookup_dict = lookup.dict()
 lookup_dict["alveolus of lung"]

By default, the "name" field is used to generate lookup keys. You can
specify another field to look up:

 lookup = tissues.lookup(tissues.ontology_id)

 lookup.uberon_0000031

##### Search terms

Search behaves in the same way as it does for registries:

 tissues.search("lung alveolus").head(3)

By default, search also covers synonyms and all other fields
containing strings:

 tissues.search("nasal sac").head(3)

Search specific field (by default, search is done on all fields
containing strings):

 tissues.search(
 "spherical outcropping of the respiratory",
 field=tissues.definition,
 ).head()

##### Standardize Tissue identifiers

Let us generate a "DataFrame" that stores a number of Tissue
identifiers, some of which corrupted:

 df_orig = pd.DataFrame(
 index=[
 "UBERON:0000000",
 "UBERON:0000005",
 "UBERON:0000001",
 "UBERON:0000002",
 "This tissue does not exist",
 ]
 )
 df_orig

We can check whether any of our values are validated against the
ontology reference:

 validated = tissues.validate(df_orig.index, tissues.name)
 df_orig.index[~validated]

##### Ontology source versions

For any given entity, we can choose from a number of versions:

 bt.Source.filter(entity="bionty.Tissue").to_dataframe()

 # only lists the sources that are currently used
 bt.Source.filter(entity="bionty.Tissue", currently_used=True).to_dataframe()

When instantiating a Bionty object, we can choose a source or version:

 source = bt.Source.filter(
 name="uberon", organism="all"
 ).first()
 tissues= bt.Tissue.public(source=source)
 tissues

The currently used ontologies can be displayed using:

 bt.Source.filter(currently_used=True).to_dataframe()